Meta Forced to Unwind $2B Manus Deal by China

Meta has launched Muse, a personal AI agent designed to handle everyday tasks like online shopping, email, and trip planning with minimal user input. The product represents Meta's latest effort to regain ground in the competitive AI market against OpenAI, Anthropic, and Google. Muse can operate autonomously once given a goal, including opening browsers, filling forms, and negotiating on behalf of users.

Meta has launched Muse Voice Transcribe, a real-time speech-to-text model priced at $0.18 per hour of audio that combines transcription, speaker diarization for 20+ speakers, and endpoint detection in a single model. The system supports over 70 languages, with 25 extensively validated for release, and handles multilingual code-switching without separate post-processing. While competitors like Speechmatics support higher speaker counts (up to 100), Meta's combination of low latency, high-capacity diarization, and aggressive pricing targets enterprise developers building meeting systems, call analytics, and ambient AI applications.

Meta unveiled Muse Voice Transcribe, a new audio AI model that transcribes speech to text and segments audio by speaker. CEO Mark Zuckerberg announced the model on Threads, noting it was trained on more than 70 hours of audio data. The model represents Meta's continued push into audio AI capabilities alongside its existing generative AI portfolio.

Researchers at Meta AI and University of Illinois Urbana-Champaign developed EvoHarness-RL, a training framework that enables smaller AI models to perform complex, long-horizon tasks by learning to dynamically manage their execution environment rather than following rigid, manually-coded instructions. The approach consolidates agent support systems into a unified Belief, Progress, and Experience workspace, allowing models to independently decide when and how to consult external state during workflows. This addresses a key limitation in current agentic systems where manual prompts and static memory structures require extensive retuning for each model upgrade.